Wei Wan1, Gaige Wang1,2,3,*, Junyu Dong1
CMES-Computer Modeling in Engineering & Sciences, Vol.130, No.3, pp. 1495-1516, 2022, DOI:10.32604/cmes.2021.017987
Abstract Adaptive Cross-Generation Differential Evolution (ACGDE) is a recently-introduced algorithm for solving multiobjective problems with remarkable performance compared to other evolutionary algorithms (EAs). However, its
convergence and diversity are not satisfactory compared with the latest algorithms. In order to adapt to the current
environment, ACGDE requires improvements in many aspects, such as its initialization and mutant operator.
In this paper, an enhanced version is proposed, namely SIACGDE. It incorporates a strengthened initialization
strategy and optimized parameters in contrast to its predecessor. These improvements make the direction of crossgeneration mutation more clearly and the ability of searching more efficiently. The experiments show… More >
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